Evaluation of the EKF-based Estimation Architectures for Data Fusion in Mobile Robots

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00214002" target="_blank" >RIV/68407700:21230/15:00214002 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/TMECH.2014.2311416" target="_blank" >http://dx.doi.org/10.1109/TMECH.2014.2311416</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TMECH.2014.2311416" target="_blank" >10.1109/TMECH.2014.2311416</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluation of the EKF-based Estimation Architectures for Data Fusion in Mobile Robots

Popis výsledku v původním jazyce

This paper presents evaluation of 4 different state estimation architectures exploiting the extended Kalman filter (EKF) for 6DOF dead reckoning of a mobile robot. The EKF is a well proven and commonly used technique for fusion of inertial data and robot?s odometry. However, different approaches to designing the architecture of the state estimator lead to different performance and computational demands. While seeking the best possible solution for the mobile robot, the nonlinear model and the error model are addressed, both with and without a complementary filter for attitude estimation. The performance is determined experimentally by means of precision of both indoor and outdoor navigation, including complex structured environment such as stairs and rough terrain. According to the evaluation, the nonlinear model combined with the complementary filter is selected as a best candidate (reaching 0.8 m RMSE and average of 4% return position error of distance driven) and implemented for rea

Název v anglickém jazyce

Evaluation of the EKF-based Estimation Architectures for Data Fusion in Mobile Robots

Popis výsledku anglicky

This paper presents evaluation of 4 different state estimation architectures exploiting the extended Kalman filter (EKF) for 6DOF dead reckoning of a mobile robot. The EKF is a well proven and commonly used technique for fusion of inertial data and robot?s odometry. However, different approaches to designing the architecture of the state estimator lead to different performance and computational demands. While seeking the best possible solution for the mobile robot, the nonlinear model and the error model are addressed, both with and without a complementary filter for attitude estimation. The performance is determined experimentally by means of precision of both indoor and outdoor navigation, including complex structured environment such as stairs and rough terrain. According to the evaluation, the nonlinear model combined with the complementary filter is selected as a best candidate (reaching 0.8 m RMSE and average of 4% return position error of distance driven) and implemented for rea

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

<a href="/cs/project/7E10044" target="_blank" >7E10044: Natural human-robot cooperation in dynamic environments</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE-ASME TRANSACTIONS ON MECHATRONICS

ISSN

1083-4435

e-ISSN

—

Svazek periodika

20

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

6

Strana od-do

985-990

Kód UT WoS článku

000352365800048

EID výsledku v databázi Scopus

2-s2.0-84908401680